Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the fashion recommendation model from Hugging Face
+model = pipeline("image-classification", model="your-huggingface-model")
+
+def recommend_outfit(image, style_pref, body_type):
+    predictions = model(image)
+    
+    # Mockup of recommended outfits based on predictions and user preferences
+    recommended_outfits = ["outfit1.jpg", "outfit2.jpg", "outfit3.jpg"]  # Replace with actual logic
+    
+    return predictions, recommended_outfits
+
+def feedback(outfit_image, like):
+    if like == "Like":
+        return "Thank you for your feedback!"
+    else:
+        return "We appreciate your input!"
+
+# Define Gradio interface for outfit recommendation
+iface = gr.Interface(
+    fn=recommend_outfit,
+    inputs=[
+        gr.inputs.Image(type="pil"),
+        gr.inputs.Textbox(label="Style Preferences"),
+        gr.inputs.Textbox(label="Body Type")
+    ],
+    outputs=[
+        gr.outputs.Label(num_top_classes=3),
+        gr.outputs.Image(type="filepath", label="Recommended Outfits")
+    ],
+    title="ChicAI: Your AI-Powered Virtual Stylist",
+    description="Upload an image of your clothing to get outfit recommendations based on current trends."
+)
+
+# Define Gradio interface for feedback mechanism
+feedback_interface = gr.Interface(
+    fn=feedback,
+    inputs=[
+        gr.inputs.Image(type="filepath", label="Outfit Image"),
+        gr.inputs.Radio(["Like", "Dislike"], label="Did you like this outfit?")
+    ],
+    outputs="text"
+)
+
+# Launch both interfaces (you may want to integrate them better in a real app)
+iface.launch()
+feedback_interface.launch()